jbaruch/face-recognition-calibration-djl
Empirical calibration for DJL face_feature (ArcFace/FaceNet 512-d) embeddings: cosine distance bands, piecewise confidence formula, enrollment quality targets. Replaces the dlib-based jbaruch/face-recognition-calibration tile for Kotlin/JVM pipelines.
81
86%
Does it follow best practices?
Impact
100%
2.17xAverage score across 2 eval scenarios
Passed
No known issues
Quality
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is an excellent skill description that is highly specific, technically precise, and well-structured. It clearly defines what the skill does with concrete mathematical thresholds, names the relevant technologies, and provides an explicit 'Use when...' clause with multiple realistic trigger scenarios including a diagnostic use case. The only minor concern is that the density of technical detail might be slightly more than needed, but it serves the purpose of disambiguation well.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: piecewise mapping with exact thresholds (d ≤ 0.30 → 1.0, d ≥ 0.65 → 0.0), enrollment averaging, L2 normalization, and identifies a specific anti-pattern. Extremely detailed and concrete. | 3 / 3 |
Completeness | Clearly answers both 'what' (compute perceptually-correct confidence from cosine distances using piecewise mapping, enrollment averaging, L2 normalization) and 'when' with an explicit 'Use when...' clause covering three distinct trigger scenarios: mapping distance to confidence, driving confidence displays, and diagnosing weak-looking results. | 3 / 3 |
Trigger Term Quality | Includes highly relevant natural keywords: 'cosine distance', 'confidence score', 'FaceNet', 'ArcFace', 'DJL face_feature', 'semaphore', 'progress bar', 'gauge', 'confidence display', and the diagnostic scenario 'why a strong-looking recognition still reads as yellow or weak'. Good coverage of terms a user working in this domain would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Extremely niche and specific to face recognition confidence scoring from cosine distances with DJL. The combination of specific thresholds, named architectures (FaceNet/ArcFace), and the particular anti-pattern makes it virtually impossible to conflict with other skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a strong, highly actionable skill that encodes hard-won calibration knowledge specific to DJL face_feature cosine distances. The executable Kotlin code, concrete distance examples, and anti-patterns section are excellent. The main weaknesses are the lack of an explicit sequenced workflow with validation checkpoints and some verbosity in the explanatory distance tables that could be condensed.
Suggestions
Add an explicit numbered workflow sequence (e.g., 1. Load model → 2. Enroll → 3. Validate enrollment distances → 4. Run recognition → 5. Verify with diagnostic logging) with validation checkpoints between steps.
Consider extracting the full pipeline code and enrollment averaging into a referenced file (e.g., PIPELINE.kt) to keep SKILL.md as a concise overview with the key formula and anti-patterns.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient and domain-specific, but includes some verbosity in the explanatory sections (e.g., the extended walkthrough of textbook formula outputs at multiple distances, and the repeated distance tables). The anti-patterns and diagnostic sections add value but could be slightly tighter. | 2 / 3 |
Actionability | Fully executable Kotlin code throughout — the piecewise formula, full DJL pipeline with translator, enrollment averaging with re-normalization, threshold logic, and diagnostic logging are all copy-paste ready with concrete, specific examples. | 3 / 3 |
Workflow Clarity | The pipeline components are clearly presented and the distinction between threshold vs. confidence is well-articulated, but there's no explicit sequenced workflow with validation checkpoints. The diagnostic section helps but is positioned as troubleshooting rather than an integrated validation step in the pipeline. | 2 / 3 |
Progressive Disclosure | The content is well-organized with clear section headers and logical progression from problem → solution → full pipeline → enrollment → thresholds → anti-patterns → diagnostics. However, the skill is fairly long (~150 lines of substantive content) with no references to external files; the full pipeline code and enrollment averaging could be split out for better navigation. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
Reviewed
Table of Contents